Are linear approximators better suited to some tasks compared to complex neural net functions?
This is just a case of supervised learning. You are trying to predict $s_{t+1}$ given $s_t$ and $a_t$, so the answer to your question depends on how complex your state dynamics are.
For example, if the state space is really complex, e.g. if your state space is an image and you want to predict the next image given the current image and an action, then linear methods are unlikely to work well.